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Optimal strategies of improving energy efficiency for an energy-intensive manufacturer considering consumer environmental awareness

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  • Jianjun Ouyang
  • Jie Fu

Abstract

Motivated by the observation that many energy-intensive manufacturers are adopting energy efficiency to deal with rapidly increasing consumer environmental awareness (CEA), this paper is the first attempt that extends CEA into the energy-saving area using several mathematical models and investigates how an energy-intensive manufacturer, facing choices of self-saving, shared savings and guaranteed savings, determines the optimal strategies of improving energy efficiency when CEA is considered. When the actual savings per unit is deterministic (DU scenario), we derive the optimal strategies of improving energy efficiency for both the manufacturer and the energy service company (ESCO) under the two energy-saving modes: self-saving and shared savings. Our results show the following: (1) CEA always has positive impacts on the optimal predicted savings per unit and the optimal profits of both the manufacturer and the ESCO; (2) interestingly, the impact of CEA on the optimal choice between energy saving modes may not exist. Furthermore, when the actual savings per unit is stochastic (UU scenario), and if the energy-intensive manufacturer introduces guaranteed savings, we find that most of the optimal strategies under the UU scenario are similar to those under the DU scenario. Finally, through numerical studies, we demonstrate the important results.

Suggested Citation

  • Jianjun Ouyang & Jie Fu, 2020. "Optimal strategies of improving energy efficiency for an energy-intensive manufacturer considering consumer environmental awareness," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1017-1033, February.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:4:p:1017-1033
    DOI: 10.1080/00207543.2019.1607977
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    Cited by:

    1. Fang Dong & Chengdong Shi & Weitong Yu, 2022. "Coordinated Decision-Making in Embedded Supply Chain from a Sustainable Development Perspective," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    2. Konstantinos Plakas & Ioannis Karampinis & Panayiotis Alefragis & Alexios Birbas & Michael Birbas & Alex Papalexopoulos, 2023. "A Predictive Fuzzy Logic Model for Forecasting Electricity Day-Ahead Market Prices for Scheduling Industrial Applications," Energies, MDPI, vol. 16(10), pages 1-21, May.
    3. Xu, Mengmeng & Lin, Boqiang, 2022. "Energy efficiency gains from distortion mitigation: A perspective on the metallurgical industry," Resources Policy, Elsevier, vol. 77(C).

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